aov                   package:base                   R Documentation

_F_i_t _a_n _A_n_a_l_y_s_i_s _o_f _V_a_r_i_a_n_c_e _M_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     Fit an analysis of variance model by a call to 'lm' for each
     stratum.

_U_s_a_g_e:

     aov(formula, data = NULL, projections = FALSE, qr = TRUE,
         contrasts = NULL, ...)

_A_r_g_u_m_e_n_t_s:

 formula: A formula specifying the model.

    data: A data frame in which the variables specified in the formula
          will be found. If missing, the variables are searched for in
          the standard way.

projections: Logical flag: should the projections be returned?

      qr: Logical flag: should the QR decomposition be returned?

contrasts: A list of contrasts to be used for some of the factors in
          the formula. These are not used for any 'Error' term, and
          supplying contrasts for factors only in the 'Error' term will
          give a warning.

     ...: Arguments to be passed to 'lm', such as 'subset' or
          'na.action'.

_D_e_t_a_i_l_s:

     This provides a wrapper to 'lm' for fitting linear models to
     balanced or unbalanced experimental designs.

     The main difference from 'lm' is in the way 'print', 'summary' and
     so on handle the fit: this is expressed in the traditional
     language of the analysis of variance rather than of linear models.

     If the formula contains a single 'Error' term, this is used to
     specify error strata, and appropriate models are fitted within
     each error stratum.

     The formula can specify multiple responses.

     Weights can be specified by a 'weights' argument, but should not
     be used with an 'Error' term, and are incompletely supported
     (e.g., not by 'model.tables').

_V_a_l_u_e:

     An object of class 'c("aov", "lm")' or for multiple responses of
     class 'c("maov", "aov", "mlm", "lm")' or for multiple error strata
     of class '"aovlist"'.  There are 'print' and 'summary' methods
     available for these.

_A_u_t_h_o_r(_s):

     The design was inspired by the S function of the same name
     described in Chambers _et al._ (1992).

_R_e_f_e_r_e_n_c_e_s:

     Chambers, J. M., Freeny, A and Heiberger, R. M. (1992) _Analysis
     of variance; designed experiments._ Chapter 5 of _Statistical
     Models in S_ eds J. M. Chambers and T. J. Hastie, Wadsworth &
     Brooks/Cole.

_S_e_e _A_l_s_o:

     'lm', 'summary.aov', 'alias', 'proj', 'model.tables', 'TukeyHSD'

_E_x_a_m_p_l_e_s:

     ## From Venables and Ripley (2002) p.165.
     N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0)
     P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0)
     K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0)
     yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5,55.0,
                62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0)
     npk <- data.frame(block=gl(6,4), N=factor(N), P=factor(P),
                       K=factor(K), yield=yield)

     ( npk.aov <- aov(yield ~ block + N*P*K, npk) )
     summary(npk.aov)
     coefficients(npk.aov)

     ## as a test, not particularly sensible statistically
     op <- options(contrasts=c("contr.helmert", "contr.treatment"))
     npk.aovE <- aov(yield ~  N*P*K + Error(block), npk)
     npk.aovE
     summary(npk.aovE)
     options(op)# reset to previous

